Stromatolites are fossilized, centimeter-to-meter scale laminated buildups formed by microbial activity. When examined, these sedimentary structures offer insights into the emergence of early life on Earth. However, before we can use stromatolites as a tool to study early life, we must understand what their morphology (e.g., shape and spatial arrangement) tells us about their formation. To date, few studies have quantified exactly how such variables affect stromatolite morphology. Here, I produce and apply morphological metrics to two-billion-year-old stromatolites to test two hypotheses: 1) the distribution of the individual constructions is non-random and 2) the space between stromatolites varies in thickness across space. To investigate these hypotheses, I use digital three-dimensional (3D) reconstructions of ancient stromatolite bedding planes from Great Slave Lake, Canada and make measurements. I identify the organizational patterns of these stromatolites using metrics such as area, width, length, aspect ratio, and circularity distributions across space, and explore whether such patterns are indicative of life. Ultimately, this work will broaden scientific understanding of stromatolite morphogenesis and the processes that drive early Earth systems; knowledge that may help us better interpret potential signs of life found elsewhere in our solar system. 

Tufas are dendritic carbonate precipitates that form in highly alkaline lakes, such as Mono Lake in California. They are used as paleoclimate archives and evidence of microbial life. One model for their growth is a process known as Diffusion Limited Aggregation (DLA). DLA occurs when there are no advective forces and diffusion is the primary means of particle transport. Branching patterns, such as those you might see in a snowflake, frost on a window, or mineral veins in a rock, are characteristic of DLA. To date, no quantitative comparisons between tufa shape (e.g., branching patterns) and DLA exist. Here, I build a computational model of DLA with the intention of comparing my outputs to real-world three-dimensional (3D) models of tufas. I aim to test whether my models are statistically similar or different to my samples. My initial efforts have successfully recreated branching morphologies with enough detail to enable this comparison. Researchers have also pointed out that fluid flow may modify the shape of tufas. Therefore, as a future step, I intend to modify my models to include an advective component and test the effects of increasing current on tufa shape.

The effects of ongoing climate change on river systems present an ever-growing cause for concern, with flooding and other potential hazards threatening millions of people who live near rivers. To investigate how river systems react to climate change, we must turn to analogous events in Earth’s sedimentary rock record. The Paleocene-Eocene Thermal Maximum (PETM) is one such analog, during which global temperatures and precipitation seasonality rose significantly. Rivers record their response to these environmental shifts through the sedimentary structures they create. For example, we can measure cross-sets, which form as rivers preserve sections of sand dunes and ripples on the riverbed, to determine whether a river was in a state of equilibrium, with a year-round stable flow, or in disequilibrium, with increased flash flooding and river channel migration. Here, I test the hypothesis that river systems shift towards disequilibrium during periods of climate change by measuring cross-sets in PETM-aged rocks of the Bighorn Basin, Wyoming. To generate a large number of accurate measurements, I use three-dimensional (3D) digital reconstructions of rock outcroppings. This study will equip river-adjacent communities with insights on how rivers evolve during climate change, and allow them to make adequate preparations for potential hazards.